Breast ultrasound image self-learning extraction method and system based on stacked noise reduction self-encoder

Abstract

The invention discloses a breast ultrasound image self-learning extraction method and system based on a stacked noise reduction self-encoder. The method comprises the steps of extracting manual shallow layer features from each ultrasound breast lesion area image ROI as a training sample to form a training sample set set_unlabeled = {x(1), x(2), ..., x(n)}, the i-th sample x(i) belonging to [0, 1]<d>, i = 1, 2, ..., n; based on the training sample set, training a first noise reduction self-encoder DAE1; after training the first noise reduction self-encoder, re-entering the training sample set, using the self-encoder trained in the step S4 to extract feature expressions obtained through hidden layer learning of all the samples to form a new sample {y(1), y(2), ..., y(n)}, and using the new sample as an input of a second noise reduction self-encoder DAE2 to train the second noise reduction self-encoder. The invention achieves extraction of breast ultrasound image features, thereby provides valuable reference opinions for clinic diagnosis, and improves the accuracy and efficiency of breast cancer diagnosis.

Description

technical field [0001] The invention relates to the technical field of feature engineering, in particular to a method and system for self-learning and extracting breast ultrasound image features based on stacked noise reduction autoencoders. Background technique [0002] Breast cancer is the most common malignant tumor in women all over the world, and about 400,000 people die of the disease every year. China is one of the countries with the fastest-growing incidence of breast cancer, especially in recent years, breast cancer has become the first malignant tumor among women in my country. The treatment effect of early breast cancer is good and can save patients' lives to a large extent, so it is becoming more and more meaningful to improve the precision and accuracy of early diagnosis of breast cancer. [0003] At present, the clinical diagnosis of breast cancer mainly uses imaging examinations such as breast ultrasound and mammography. Diagnosers analyze the images based on...

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Problems solved by technology

[0006] To this end, it is necessary to provide a breast ultrasound image feature self-learning extraction scheme based on stacked denoising autoencoders to solve the prob

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